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from easydict import EasyDict
ant_trex_sac_config = dict(
exp_name='ant_trex_sac_seed0',
env=dict(
manager=dict(shared_memory=True, reset_inplace=True),
env_id='Ant-v3',
norm_obs=dict(use_norm=False, ),
norm_reward=dict(use_norm=False, ),
collector_env_num=1,
evaluator_env_num=8,
n_evaluator_episode=8,
stop_value=6000,
),
reward_model=dict(
type='trex',
min_snippet_length=30,
max_snippet_length=100,
checkpoint_min=1000,
checkpoint_max=9000,
checkpoint_step=1000,
learning_rate=1e-5,
update_per_collect=1,
# Users should add their own model path here. Model path should lead to a model.
# Absolute path is recommended.
# In DI-engine, it is ``exp_name/ckpt/ckpt_best.pth.tar``.
expert_model_path='model_path_placeholder',
# Path where to store the reward model
reward_model_path='abs_data_path + ./ant.params',
continuous=True,
# Path to the offline dataset
# See ding/entry/application_entry_trex_collect_data.py to collect the data
offline_data_path='abs_data_path',
),
policy=dict(
cuda=True,
random_collect_size=10000,
model=dict(
obs_shape=111,
action_shape=8,
twin_critic=True,
action_space='reparameterization',
actor_head_hidden_size=256,
critic_head_hidden_size=256,
),
learn=dict(
update_per_collect=1,
batch_size=256,
learning_rate_q=1e-3,
learning_rate_policy=1e-3,
learning_rate_alpha=3e-4,
ignore_done=False,
target_theta=0.005,
discount_factor=0.99,
alpha=0.2,
reparameterization=True,
auto_alpha=False,
),
collect=dict(
n_sample=1,
unroll_len=1,
),
command=dict(),
eval=dict(),
other=dict(replay_buffer=dict(replay_buffer_size=1000000, ), ),
),
)
ant_trex_sac_config = EasyDict(ant_trex_sac_config)
main_config = ant_trex_sac_config
ant_trex_sac_create_config = dict(
env=dict(
type='mujoco',
import_names=['dizoo.mujoco.envs.mujoco_env'],
),
env_manager=dict(type='subprocess'),
policy=dict(
type='sac',
import_names=['ding.policy.sac'],
),
replay_buffer=dict(type='naive', ),
)
ant_trex_sac_create_config = EasyDict(ant_trex_sac_create_config)
create_config = ant_trex_sac_create_config
if __name__ == "__main__":
# or you can enter `ding -m serial -c ant_trex_sac_config.py -s 0`
from ding.entry import serial_pipeline_trex
serial_pipeline_trex((main_config, create_config), seed=0)
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